Abstract
Recently, HTTP-based video streaming traffic has continued to increase. Therefore, video service providers have been using HTTP-based adaptive streaming (HAS) technology to reduce the traffic load of the HTTP server. Accordingly, many adaptive bit rate (ABR) schemes have been proposed to provide a high quality of experience (QoE) to video service clients. In this paper, we propose a new ABR scheme using an adaptive network-based fuzzy inference system (ANFIS), which is one of the neuro-fuzzy structures. The proposed scheme learns optimal fuzzy parameters by using (1) the learning ability of ANFIS and (2) the video streaming data providing high QoE to clients. Then, the bit rate of the next segment is determined according to these trained parameters. In the simulation using NS-3, we show that the proposed scheme selects the appropriate bit rate under various wireless network conditions and provides better QoE to clients than the existing schemes.
Highlights
IntroductionIt is expected to increase more than ever due to the expansion of streaming audio and video adoption and the emergence of state-of-the-art video technologies such as 4K, high-dynamic-range video, and virtual reality [1]
In recent years, Hypertext transfer protocol (HTTP)-based video streaming traffic has consistently increased
It is expected to increase more than ever due to the expansion of streaming audio and video adoption and the emergence of state-of-the-art video technologies such as 4K, high-dynamic-range video, and virtual reality [1]. This traffic growth has led to the adoption of HTTP-based adaptive streaming (HAS) technology by video service providers to reduce the traffic load of the HTTP server
Summary
It is expected to increase more than ever due to the expansion of streaming audio and video adoption and the emergence of state-of-the-art video technologies such as 4K, high-dynamic-range video, and virtual reality [1]. This traffic growth has led to the adoption of HTTP-based adaptive streaming (HAS) technology by video service providers to reduce the traffic load of the HTTP server. HAS technology encodes video at multiple bit rates and divides the video at each bit rate into multiple segments with a fixed playback duration Data such as media representation, segment duration, and segment URLs are stored as a file in the HTTP server. HAS technology reduces the traffic load on the server and saves bandwidth resources by sending only a single segment for each client request rather than the entire video [2]
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